In general, enterprises spend a significant amount of time and resources on help desk for resolving issues or tickets raised by users. The tickets are resolved by either employees or external users of an enterprise's services. The help desk provides support for resolving tickets and may resolve 5% to 20% of the incident tickets. The enterprises include Level 1 help desk employees for addressing IT issues. More recently, an enterprise help desk management provides virtual agents or automated solutions to resolve tickets. Such solutions accept a user query and provide information or a solution based on error symptoms given by a user.
To virtualize the Level 1 helpdesk, it may be required to accurately classify user problems or issues into a correct issue category. Also, predefined issue categorizations of incident management systems are usually not fine-grained, hence defining issue hierarchy needs to be done from scratch. This requires an increased amount of manual work to identify various issues under different applications, issue symptoms, and corresponding solution.
Enterprise help desk tickets history might provide valuable inputs in identifying the user problems and corresponding symptoms. Also, the enterprise help desk tickets history can be converted into an issue database. However, volume of the tickets is usually high for manual inspection of the tickets and to derive required information. One standard method for gathering insights from ticket history is data clustering. However, data clustering provides indicative clusters at a high level and lot of manual efforts are still required to obtain fine-grained clusters.